By P. P. M. Pompe, A. J. Feelders (auth.), Phillip Ein-Dor (eds.)
In the earlier a long time a number of researchers have constructed statistical versions for the prediction of company financial ruin, e. g. Altman (1968) and Bilderbeek (1983). A version for predicting company financial disaster goals to explain the relation among financial disaster and a couple of explanatory monetary ratios. those ratios will be calculated from the knowledge contained in a company's annual document. The is to procure a mode for well timed prediction of financial disaster, a so final objective referred to as "early caution" procedure. extra lately, this topic has attracted the eye of researchers within the zone of computer studying, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This study is mostly directed on the comparability of desktop studying tools, reminiscent of induction of type bushes and neural networks, with the "standard" statistical tools of linear discriminant research and logistic regression. In previous learn, Feelders et al. (1994) played an analogous comparative research. The tools used have been linear discriminant research, selection bushes and neural networks. We used an information set which contained 139 annual reviews of Dutch business and buying and selling businesses. The experiments confirmed that the envisioned prediction errors of either the choice tree and neural community have been lower than the predicted blunders of the linear discriminant. therefore it sounds as if we will achieve through exchanging the "traditionally" used linear discriminant by means of a extra versatile class strategy to are expecting company financial disaster. the information set utilized in those experiments was once very small however.
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Additional resources for Artificial Intelligence in Economics and Managment: An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8–10, 1996
If we consider individual business objects to be the embodiment of business knowledge within our system, meta-knowledge becomes knowledge about our objects. Prolog Business Objects in a Three-tier Architecture 27 What can we know about an object in general? We can know its name, its location, its domain, and what tasks it can perform or goals it can solve. In a distributed client-server environment we can also know the computational requirements of the business object and the computational resources of the machine on which it is currently resident.
We present the architecture of a Prolog-based business logic object, discuss its advantages, and how it can be integrated with the other two tiers. In the discussion we focus on the meta-level characteristics of Prolog and how we can take advantage of these characteristics in both the individual objects and in the architecture as a whole. 2. The Three-tier Model of Client/Server Business Applications 1. In general, a three-tier business application can do three things: Communicate with the external world through External Interface Objects (EIO).
It then invokes price_discount/5 to assigns a discount level of 10, 15, or 20% based on the current inventory level of the item, and based on an inventory analysis performed by a different business object. */ pricing_discount (Item, Quantity, Price, Discount) <-inventory_level (Item, Level), price_discount (Item, Quantity, Price, Discount, Level) . price_discount(Item, Quantity, Price, 0%, Level) level_analysis (Quantity, Level, none). price_discount (Item, Quantity, Price, 10%, Level) level_analysis (Quantity, Level, low).
Artificial Intelligence in Economics and Managment: An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8–10, 1996 by P. P. M. Pompe, A. J. Feelders (auth.), Phillip Ein-Dor (eds.)